中文摘要：

经验模式分解（Empirical Mode Decomposition，EMD）是一种数据驱动的自适应非线性时变信号分解方法，可以把数据分解成具有物理意义的少数几个模式函数分量．本文总结归纳了一维EMD、二维EMD方面的主要工作，比较了不同方法存在的优点与不足，指出了EMD研究存在的难题和瓶颈，并给出了EMD研究与应用的发展趋势．

英文摘要：

Empirical Mode Decomposition （EMD） is a decomposition algorithm which is used to analyze nonlinear and time-varying signal.Different from the traditional signal analysis method, the decomposition is data-driven and serf-adaptive. A review work about the current development of one dimensional EMD and Bidimensional EMD is introduced. At first, some basic concepts and main algorithm ideas are described. Then the advantages and shortages of EMD are discussed. At the end of the paper, several problems which are waiting to be solved are listed.